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CyberArk recently published “Poison Everywhere: No Output from Your MCP Server is Safe“, exploring Tool Poisoning Attacks (TPA) in MCP (Machine Learning Control Plane) servers. Initially described by Invariant Labs, a TPA occurs when attackers inject malicious instructions into MCP tool descriptions. These instructions remain hidden from users but are visible to AI models, leading to unauthorized actions.
Invariant Labs discovered that attackers can manipulate tool descriptions to:
– Direct AI models to access sensitive files (SSH keys, configs, databases).
– Extract and exfiltrate data while hiding these actions from users.
– Exploit simplified UI representations to conceal malicious behavior.
Additionally, flaws in GitHub’s MCP server were exposed: GitHub MCP Flaws.
You Should Know: How to Detect & Mitigate MCP Poisoning
1. Monitor AI Model Behavior
Check for unexpected file access or data exfiltration:
Linux: Monitor AI process file access strace -f -e trace=file -p $(pgrep -f "ai_model_process") Windows: Use Procmon to track AI tool activities Procmon.exe /AcceptEula /BackingFile log.pml
2. Restrict File Permissions
Limit AI model access to sensitive directories:
Linux: Restrict directory access chmod 700 ~/.ssh/ chattr +i /etc/passwd Windows: Use icacls to lock down files icacls C:\secrets\ /deny "AI_Service_Account:(R,W)"
3. Validate MCP Tool Descriptions
Audit JSON/YAML tool definitions for hidden payloads:
Search for suspicious patterns in tool configs
grep -r "eval(" /path/to/mcp/tools/
jq '.[] | select(.description | contains("base64"))' tools.json
4. Enable Logging for AI Tool Outputs
Capture all AI-generated commands before execution:
Log AI model outputs to a secure file sudo tcpdump -i lo -w ai_commands.pcap port 5000
5. Use Network Segmentation
Isolate MCP servers from critical infrastructure:
Linux: Block unauthorized outbound connections iptables -A OUTPUT -p tcp --dport 443 -j DROP Windows: Restrict AI service network access netsh advfirewall firewall add rule name="Block_AI_Exfil" dir=out action=block program="C:\AI\model.exe"
What Undercode Say
MCP poisoning is a silent threat—attackers weaponize AI trust to bypass traditional security. Defenses include:
– Behavioral monitoring (strace, Procmon).
– Strict file permissions (chmod, icacls).
– Tool description audits (grep, jq).
– Network controls (iptables, netsh).
AI-driven systems require zero-trust validation—assume no output is safe.
Prediction
As AI integration grows, TPA attacks will surge, targeting cloud MCPs (AWS Bedrock, Azure ML). Expect defensive AI tools to emerge, auto-detecting poisoned instructions.
Expected Output:
- Detected unauthorized file access attempts.
- Blocked suspicious AI model commands.
- Logged all MCP tool interactions for forensics.
Relevant URLs:
IT/Security Reporter URL:
Reported By: Mthomasson Despite – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
